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Towards personalised medicine – assessing risks and benefits for individual patientsProf Julia Hippisley-Cox, University of Nottingham, Tony Mitchell Lecture15th May 2013

+Acknowledgements

Co-authors Drs Carol Coupland, Peter Brindle, John Robson

QResearch database

University of Nottingham

EMIS & contributing practices & user group

ClinRisk Ltd (software)

Oxford University (independent validation, Prof Altman’s team)

+Outline

QResearch database +linked data

General approach to risk prediction

QRISK2

QDiabetes

QIntervention

QFracture

Any questions

+QResearch Database

One of the worlds largest and richest research databases

Over 700 general practices across the UK, 14 million patients

Joint venture between EMIS (largest GP supplier > 55% practices) and University of Nottingham

Patient level pseudonymised database for research

Available for peer reviewed academic research where outputs made publically available

Data from 1989 to present day.

+Information on QResearch – GP derived data

Demographic data – age, sex, ethnicity, SHA, deprivation

Diagnoses

Clinical values –blood pressure, body mass index

Laboratory tests – FBC, U&E, LFTs etc

Prescribed medication – drug, dose, duration, frequency, route

Referrals

Consultations

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QResearch database already linked to deprivation data in 2002 cause of death data in 2007

Very useful for research better definition & capture of outcomes Health inequality analysis Improved performance of QRISK2 and similar scores

Developed new technique for data linkage using pseudonymised data

QResearch Data Linkage Project

+www.openpseudonymiser.org

Scrambles NHS number BEFORE extraction from clinical system

Takes NHS number + project specific encrypted ‘salt code’

One way hashing algorithm (SHA2-256) Cant be reversed engineered Applied twice in two separate locations before

data leaves source Apply identical software to external dataset Allows two pseudonymised datasets to be linked Open source – free for all to use

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+QResearch Database + data linked in 2013

Data source Time period data available

GP data 1989-

ONS cause of death 1997-

ONS cancer registration 1997-

HES Outpatient data 1997-

HES Inpatient data 1997-

HES A&E data 2007-

+Clinical Research Cycle

Clinical practice &

benefit

Clinical questions

Research +

innovation

Integration into clinical

systems

+A new family of Risk Prediction tools Individual assessment

Who is most at risk of preventable disease? Who is likely to benefit from interventions? What is the balance of risks and benefits for my patient? Enable informed consent and shared decisions

Population level Risk stratification Identification of rank ordered list of patients for recall or

reassurance

GP systems integration Allow updates tool over time, audit of impact on services and

outcomes

+Criteria for choosing clinical outcomes

Major cause morbidity & mortality Represents real clinical need Related intervention which can be targeted Related to national priorities (ideally) Necessary data in clinical record Can be implemented into everyday clinical practice

+Change in research question

Leads to Novel application of existing methods Development of new methods Better utilisation different data sources

Leads to Lively academic debate! Changes in policy and guidance New utilities to implement research findings (hopefully) Better patient care

+Published & validated scores

scores outcome Web link

QRISK2 CVD www.qrisk.org

QDiabetes Type 2 diabetes www.qdiabetes.org

QStroke Ischaemia stroke www.qstroke.org

QKidney Moderate/severe renal failure

www.qkidney.org

QThrombosis VTE www.qthrombosis.org

QFracture Osteoporotic fracture www.qfracture.org

QIntervention Risks benefits interventions to lower CVD and diabetes risk

www.qintervention.org

QCancer Detection common cancers www.qcancer.org

+Vascular Risk Engine: Requirements Identify patients at high risk of vascular disease

CVD Diabetes Stage 3b,4, 5 Kidney Disease

Assessment of individual’s risk profile

Risks and benefits of interventions Weight loss Smoking cessation BP control Statins

+Why integrated tool CVD, diabetes, CKD?

Many of the risk factors over overlap

Many of the interventions overlap

But different patients have different risk profiles Smoking biggest impact on CVD risk Obesity has biggest impact on diabetes risk Blood pressure biggest impact on CKD risk

Help set individual priorities

Development of personalised plans and achievable target

+Primary prevention CVD:(slide from NICE website)

Offer information about: • absolute risk of vascular disease • absolute benefits/harms of an

intervention

Information should:• present individualised risk/benefit

scenarios• present absolute risk of events

numerically• use appropriate diagrams and text

+Challenge: to develop a new CVD risk score for use in UK

New cardiovascular disease risk score

Calibrated to UK population

Use routinely collected GP data

Include additional known risk factors

(eg family history, deprivation)

Better calibration and discrimination than Framingham

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Aim for QRISK

+Why a new CVD risk score?

Framingham has many strengths but some limitations: Small cohort (5,000 patients) from one

American town Almost entirely white Developed during peak incidence CVD in US Doesn’t include certain risk factors

(body mass index, family history, blood pressure treatment, deprivation)

Over predicts CVD risk by up to 50% in European populations

Underestimates risk in patients from deprived areas

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+QRisk1 risk factors

Traditional risk factors Age, sex, smoking status Systolic blood pressure Ratio of total serum cholesterol/high density

lipoprotein (HDL) cholesterol

New risk factors Deprivation (Townsend score output area) Family history of premature CVD 1st degree

relative aged < 60 years Body mass index Blood pressure treatment

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+Model Derivation

Separate models in males and females

Cox regression analysis

Fractional polynomials to model

non-linear risk relationships

Multiple imputation of missing values

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+Derivation of QRISK2 Score

Derivation cohort 355 practices; 1,591,209 patients; 96,709 events

Additional risk factors: ethnic group type 2 diabetes, treated hypertension,

rheumatoid arthritis, renal disease, atrial fibrillation

Interactions with age

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J Hippisley-Cox, C Coupland, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 2008; 336: 1475-1482

+Results

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Hippisley-Cox J et al. BMJ 2008;336:1475-1482

+Interactions

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Fig 1 Impact of age on hazard ratios for cardiovascular disease risk factors using the QRISK2 model.

Hippisley-Cox J et al. BMJ 2008;336:1475-1482

+Validation

Separate sample of 176 QResearch practices; 750,232 patients; 43,396 events

Validation statistics (for survival data)

D statistic1 (discrimination) R squared (% variation explained) Predicted vs. observed CVD events Clinical impact in terms of reclassification of patients

into high/low risk

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1 Royston and Sauerbrei. A new measure of prognostic separation in survival data. Stat Med 2004; 23: 723-748.

+Calculation of risk scores

Risk scores calculated in validation dataset

Risk score calculation: Used coefficients for risk factors obtained from Cox

model using multiple imputed data Combined these with patient characteristics in

validation data to give prognostic index Combined with baseline survival function estimated at

10 years to give estimated risk of CVD at 10 years for each person

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Validation statistics  QRISK2 Framingham

Women

D statistic 1.80 1.63

R2 43.5% 38.9%

Men

D statistic 1.62 1.50

R2 38.4% 34.8%

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Hippisley-Cox J et al. BMJ 2008;336:1475-1482

+Reclassification

112,156 patients (15.0%) classified as high risk (≥20%) using Framingham

78,024 patients (10.4%) classified as high risk (≥20%) using QRISK2

41.1% of patients classified as high risk using Framingham would be classified as low risk using QRISK2. Their observed 10 year risk was 16.6% (95% CI 16.1% to 17.0%).

15.3% of patients classified as high risk using QRISK2 would be classified as low risk using Framingham. Their observed 10 year risk was 23.3% (95% CI 22.2% to 24.4%).

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+QRISK2 web calculator: www.qrisk.org

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+ 30

QRISK2 web calculator

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QRISK2 web calculator

External validation using THIN database

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Additional validation carried out using the THIN database Based on practices in UK using Vision system

One validation carried out by QRISK authors Hippisley-Cox J et al. The performance of the QRISK cardiovascular risk

prediction algorithm in an independent UK sample of patients from general practice: a validation study. Heart 2007:hrt.2007.134890.

An independent validation carried out by a separate group Collins GS, Altman DG. An independent and external validation of

QRISK2 cardiovascular disease risk score: a prospective open cohort study. BMJ 2010;340:c2442

External validation using THIN database

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QRESEARCH:QRISK2

THIN:QRISK2

Women

ROC statistic 0.817 (0.814 to 0.820)

0.801

D statistic (95% CI) 1.795 (1.769 to 1.820)

1.66 (1.56 to 1.76)

R2 statistic (95% CI) 43.5 (42.8 to 44.2) 39.5 (36.6 to 42.4)

Men

ROC statistic 0.792 (0.789 to 0.794)

0.773

D statistic (95% CI) 1.615 (1.594 to 1.637)

1.45 (1.31 to 1.59)

R2 statistic (95% CI) 38.4 (37.8 to 39.0) 33.3 (28.9 to 37.8)

Collins GS, Altman DG. An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study. BMJ 2010;340:c2442

Annual updates to QRISK2 34

Reasoning: Changes in population characteristics –

e.g. incidence of cardiovascular disease is falling; obesity is rising; smoking rates are falling

Improvements in data quality - recording of predictors and clinical outcomes becomes more complete over time (e.g. ethnic group now 50%).

Inclusion of new risk factors Changes in requirements for how the risk

prediction scores can be used - e.g. changes in age ranges.

+QRISK2 in national guidelines

+QRISK2 in clinical settings

+QRISK2 across the world source Google Analytics 8th May 2011-6th May 2013

Last 2 years 0.5 million

uses 169

countries

+QDiabetes– risk of Type 2 diabeteswww.qdiabetes.org

Predicts risk of type 2 diabetes

Published in BMJ (2009)

Independent external validation by Oxford University

Needed as epidemic of diabetes & obesity

Evidence diabetes can be prevented

Evidence that earlier diagnoses associated with better prognosis.

+QDiabetes in NICE (2012)

Preventing type 2

diabetes - risk

identification &

interventions for

individuals at high risk

2012

• Risk assessment recommended include QDiabetes

• Individual assessment and also batch processing

• Includes deprivation & ethnicity• Ages 25-84• Efficient as 2 extra questions on

top of QRISK• www.qintervention.org • Integrated into EMIS Web• Evaluation in London and

Berkshire

+Risks and Benefits of Statins

Two recent papers: Unintended effects statins (Hippisley-Cox & Coupland, BMJ,

2010) Individualising Risks & Benefits of Statins (Hippisley-Cox &

Coupland, Heart, 2010)

Conclusions: New tools to quantify likely benefit from statins New tools to identify patients who might get rare adverse

effects eg myopathy for closer monitoring

+Background to Benefits of Statins Intended benefits - reduction in CVD risk

Possible unintended benefits Thrombosis Rheumatoid arthritis Cancer Fractures Parkinson’s disease Dementia

+Statin - CVD benefit

Three methods Direct analysis of QR data change in CVD risk Indirect analysis - changes in lipid levels Synthesis of Clinical Trials

Results All three methods broadly agree 20-30% reduction in risk 1st two methods can be individualised

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Statin – adverse effects Confirmed increased risk of

Acute renal failure Liver dysfunction Serious myopathy Cataract

Class effect

Dose response for kidney failure & liver dysfunction

Risk persists during Rx

Highest risk in 1st year

Resolves within a year of stopping

+So the task in the consultation is to: Undertake clinical assessment

Work out individual’s risk of disease

Calculate expected risks and benefits from interventions

Explain risks and benefits to an individual in a way they can understand

Draw some diagrams

All within 10 minutes!

+ Qintervention www.qintervention.org

+

Osteoporosis major cause preventable morbidity & mortality.

300,000 osteoporosis fractures each year

30% women over 50 years will get vertebral fracture

20% hip fracture patients die within 6/12 50% hip fracture patients lose the ability to live

independently 2 billion is cost of annual social and hospital

care

QFracture: Background

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Effective interventions exist to reduce fracture risk

Challenge is better identification of high risk patients likely to benefit

Avoid over treatment in those unlikely to benefit or who may be harmed

Some guidelines recommend BMD but expensive and not very specific

QFracture: challenge

+QFracture in national guidelines

Published August 2012

Assess fracture risk all women 65+ and all men 75+

Assess fracture risk if risk factors

Estimate 10 year fracture risk using QFracture or FRAX

Consider use of medication to reduce fracture risk

+Two new indicators recommended QOF 2013 for Rheumatoid ArthritisID indicator Comments

NM56 % patients with RA 30-84 years who have had a CVD risk assessment using a CVD risk assessment for RA in last 15/12

QRISK2 only CVD risk tool - 30-84 yrs- adjusted for RA

NM57 % of patients with RA 50-90yrs with rheumatoid arthritis who have had fracture risk assessment using tool adjusted for RA in last 27 months

NICE recommends QFracture

http://www.nice.org.uk/media/D76/FE/NICEQOFAdvisoryCommittee2012SummayRecommendations.pdf

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• Example: • 64 year old women• History of falls• Asthma• Rheumatoid

arthritis• On steroids• 10% risk hip

fracture• 20% risk of any

fracture

QFracture Web calculator www.qfracture.org

+Our scores on the app store

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Thank you for listening

Questions & Discussion